Production-Grade Knowledge Graph AI Systems : Scaling, Evaluating, Securing, and Operating Graph-Powered LLM Applications in Real-World Environments
Language: English
Published by Independently Published Sep 2026, 2026
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
Condition: New
£ 41.82
Quantity: 2 available
Add to basketItem description from seller
Neuware - Most AI systems never fail in development.They fail in production.They work in controlled environments but break under real-world pressure.They scale in theory but collapse under load.They generate results but lack reliability, security, and control.Production-Grade Knowledge Graph AI Systems is the final step in building AI systems that don't just work but operate, scale, and survive in real-world environments.This book focuses on what truly separates experimental AI from enterprise-ready systems: engineering discipline, operational excellence, and system reliability.You will learn how to take advanced graph-powered AI architectures and transform them into robust, scalable, and production-ready platforms capable of handling real-world complexity.Inside this book, you will learn how to: - Design distributed architectures for large-scale knowledge graph AI systems- Optimize performance, latency, and cost across multi-stage pipelines- Implement observability, monitoring, and debugging for AI systems in production- Build evaluation frameworks to measure accuracy, reliability, and system quality- Secure graph and LLM integrations against real-world threats- Manage data integrity, governance, and lifecycle at scale- Deploy systems using modern infrastructure and CI/CD pipelines- Continuously improve systems through feedback loops and iterative optimizationMore importantly, you will learn how to think like an engineer responsible for systems that cannot fail systems that must deliver consistent, reliable results under pressure.This book does not focus on theory.It focuses on what breaks, why it breaks, and how to design systems that don't.By the end, you will understand how to move beyond experimentation and build AI systems that are: - Reliable under load- Observable and debuggable- Secure and compliant- Cost-efficient and scalable- Maintainable over timeBook 1 gave you the foundation, and Book 2 gave you intelligence, this book gives you control.The ability to take everything you've built and make it work in the real world.Go beyond prototypes.Build systems that last.…
Seller Inventory # 9798173082985
- Title
- Production-Grade Knowledge Graph AI Systems : Scaling, Evaluating, Securing, and Operating Graph-Powered LLM Applications in Real-World Environments
- Author
- Cyran Devereux
- Publisher
- Independently Published Sep 2026
- Publication year
- 2026
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 13
- 9798173082985
- Item weight
- 288 grams
- Dimensions
- 254x178x9 mm
- Series
- Book 3 of 3: Mastering Knowledge Graph AI Systems
Most AI systems never fail in development.
They fail in production.
They work in controlled environments but break under real-world pressure.
They scale in theory but collapse under load.
They generate results but lack reliability, security, and control.
Production-Grade Knowledge Graph AI Systems is the final step in building AI systems that don’t just work but operate, scale, and survive in real-world environments.
This book focuses on what truly separates experimental AI from enterprise-ready systems: engineering discipline, operational excellence, and system reliability.
You will learn how to take advanced graph-powered AI architectures and transform them into robust, scalable, and production-ready platforms capable of handling real-world complexity.
Inside this book, you will learn how to:
-
Design distributed architectures for large-scale knowledge graph AI systems
-
Optimize performance, latency, and cost across multi-stage pipelines
-
Implement observability, monitoring, and debugging for AI systems in production
-
Build evaluation frameworks to measure accuracy, reliability, and system quality
-
Secure graph and LLM integrations against real-world threats
-
Manage data integrity, governance, and lifecycle at scale
-
Deploy systems using modern infrastructure and CI/CD pipelines
-
Continuously improve systems through feedback loops and iterative optimization
More importantly, you will learn how to think like an engineer responsible for systems that cannot fail systems that must deliver consistent, reliable results under pressure.
This book does not focus on theory.
It focuses on what breaks, why it breaks, and how to design systems that don’t.
By the end, you will understand how to move beyond experimentation and build AI systems that are:
-
Reliable under load
-
Observable and debuggable
-
Secure and compliant
-
Cost-efficient and scalable
-
Maintainable over time
Book 1 gave you the foundation, and Book 2 gave you intelligence, this book gives you control.
The ability to take everything you’ve built and make it work in the real world.
Go beyond prototypes.
Build systems that last.
"Synopsis" may belong to another edition of this title.
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